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      Methods to estimate effective population size using pedigree data: Examples in dog, sheep, cattle and horse

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          Abstract

          Background

          Effective population sizes of 140 populations (including 60 dog breeds, 40 sheep breeds, 20 cattle breeds and 20 horse breeds) were computed using pedigree information and six different computation methods. Simple demographical information (number of breeding males and females), variance of progeny size, or evolution of identity by descent probabilities based on coancestry or inbreeding were used as well as identity by descent rate between two successive generations or individual identity by descent rate.

          Results

          Depending on breed and method, effective population sizes ranged from 15 to 133 056, computation method and interaction between computation method and species showing a significant effect on effective population size ( P < 0.0001). On average, methods based on number of breeding males and females and variance of progeny size produced larger values (4425 and 356, respectively), than those based on identity by descent probabilities (average values between 93 and 203). Since breeding practices and genetic substructure within dog breeds increased inbreeding, methods taking into account the evolution of inbreeding produced lower effective population sizes than those taking into account evolution of coancestry. The correlation level between the simplest method (number of breeding males and females, requiring no genealogical information) and the most sophisticated one ranged from 0.44 to 0.60 according to species.

          Conclusions

          When choosing a method to compute effective population size, particular attention should be paid to the species and the specific genetic structure of the population studied.

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          Most cited references20

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          Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle.

          When a genetic marker and a quantitative trait locus (QTL) are in linkage disequilibrium (LD) in one population, they may not be in LD in another population or their LD phase may be reversed. The objectives of this study were to compare the extent of LD and the persistence of LD phase across multiple cattle populations. LD measures r and r(2) were calculated for syntenic marker pairs using genomewide single-nucleotide polymorphisms (SNP) that were genotyped in Dutch and Australian Holstein-Friesian (HF) bulls, Australian Angus cattle, and New Zealand Friesian and Jersey cows. Average r(2) was approximately 0.35, 0.25, 0.22, 0.14, and 0.06 at marker distances 10, 20, 40, 100, and 1000 kb, respectively, which indicates that genomic selection within cattle breeds with r(2) >or= 0.20 between adjacent markers would require approximately 50,000 SNPs. The correlation of r values between populations for the same marker pairs was close to 1 for pairs of very close markers (<10 kb) and decreased with increasing marker distance and the extent of divergence between the populations. To find markers that are in LD with QTL across diverged breeds, such as HF, Jersey, and Angus, would require approximately 300,000 markers.
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            F-statistics and analysis of gene diversity in subdivided populations.

            M Nei (1977)
            It is show that Wright's F-statistics can be defined as ratios of gene diversities of heterozygosities rather than as the correlations of uniting gametes. This definition is applicable irrespective of the number of alleles involved or whether there is selection or not. The relationship between F-statistics and Nei's gene diversity analysis is discussed.
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              Estimation of historical effective population size using linkage disequilibria with marker data.

              Theory hypothesizes that the rate of decline in linkage disequilibrium (LD) as a function of distance between markers, measured by r(2), can be used to estimate effective population size (N(e)) and how it varies over time. The development of high-density genotyping makes feasible the application of this theory and has provided an impetus to improve predictions. This study considers the impact of several developments on the estimation of N(e) using both simulated and equine high-density single-nucleotide polymorphism data, when N(e) is assumed to be constant a priori and when it is not. In all models, estimates of N(e) were highly sensitive to thresholds imposed upon minor allele frequency (MAF) and to a priori assumptions on the expected r(2) for adjacent markers. Where constant N(e) was assumed a priori, then estimates with the lowest mean square error were obtained with MAF thresholds between 0.05 and 0.10, adjustment of r(2) for finite sample size, estimation of a [the limit for r(2) as recombination frequency (c) approaches 0] and relating N(e) to c (1 - c/2). The findings for predicting N(e) from models allowing variable N(e) were much less clear, apart from the desirability of correcting for finite sample size, and the lack of consistency in estimating recent N(e) (<7 generations) where estimates use data with large c. The theoretical conflicts over how estimation should proceed and uncertainty over where predictions might be expected to fit well suggest that the estimation of N(e) when it varies be carried out with extreme caution. © 2012 Blackwell Verlag GmbH.
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                Author and article information

                Contributors
                Journal
                Genet Sel Evol
                Genet. Sel. Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central
                0999-193X
                1297-9686
                2013
                2 January 2013
                : 45
                : 1
                : 1
                Affiliations
                [1 ]AgroParisTech, UMR1313 Génétique Animale et Biologie Intégrative, 16 rue Claude Bernard, F-75321, Paris 05, France
                [2 ]INRA, UMR1313 Génétique Animale et Biologie Intégrative, Domaine de Vilvert, F-78352, Jouy-en-Josas, France
                [3 ]AgroParisTech, UMR518 Mathématiques et Informatique Appliquées, 16 rue Claude Bernard, F-75321, Paris 05, France
                [4 ]IFCE, F-61310, Le Pin au Haras, France
                [5 ]Institut de l’Elevage, 149 rue de Bercy, F-75595, Paris 12, France
                Article
                1297-9686-45-1
                10.1186/1297-9686-45-1
                3599586
                23281913
                d2a84348-d0cc-4376-9552-8b49686509ba
                Copyright ©2013 Leroy et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 June 2012
                : 30 November 2012
                Categories
                Research

                Genetics
                Genetics

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